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Preparing the metrics for evaluation of SynthRAD2023 Grand Challenge
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• Goal
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Function Descriptions
• Roadmap
• Contributing
• License
• Contact

Assess the quality of the synthetic computed tomography (sCT) images against CT. N.B. At the moment only the image similarity metrics are usable. Dose metrics will be updated when the validation phase is open.

To get a local copy up and running follow these simple steps.

1. Clone the repo

git clone https://github.com/SynthRAD2023/metrics.git

or

git clone [email protected]:SynthRAD2023/metrics.git

• numpy

pip install -r requirements.txt

The metrics are computed in two files: image_metrics.py and dose_metrics.py. These compute respectively,

• The image similarity between the ground-truth CT and the synthetic CT. Thes metrics include the mean squared error (MSE), peak signal to noise ratio (PSNR), and structural similarity (SSIM).
• The metrics to compare the dose delivered to the ground truth and the synthetic CT. These metrics include the mean absolute dose (MAE), dose-volume histogram difference (DVH), and the gamma pass rate.

In general, any function can be used in the following way.

a(input, output)

description:
compute the metric a (e.g., mse, psnr, ssim) between input and output

arguments:
input: The numpy array of the ground-truth image
output: The numpy array of the predicted image

All metrics can be computed by using the score_patient, which loads the data and returns all metrics:

    metrics = ImageMetrics()
    ground_truth_path = "path/to/ground_truth.mha"
    predicted_path = "path/to/prediction.mha"
    mask_path = "path/to/mask.mha"
    print(metrics.score_patient(ground_truth_path, predicted_path, mask_path))

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the GNU General Public License v3.0. See LICENSE for more information.

Matteo Maspero - @matteomasperonl - [email protected]

Project Link: https://github.com/SynthRAD2023/metrics